Contents
Introduction
- Overview of AI in Medical Imaging: AI applications in healthcare are rapidly expanding especially in disease diagnosis. Medical fields involving imaging such as Pathology, Radiology, Dermatology are benefiting from AI. In radiology, doctors assess the reports to monitor and evaluate the disease, and such assessments are based on the doctor’s education and experience. Whereas, AI can be used to automate the process to provide more accurate and reproducible results with good quality. Among the imaging procedures, X Ray is the most common diagnostics. Doctors use it to visualize fractures and diseases like pneumonia, lung diseases, and even cancer. AI can assist doctors in accurately diagnosing these diseases.
- Importance of X-Ray Diagnosis in Healthcare: X-ray diagnosis is important to diagnose diseases early, and to provide timely treatment. X-ray diagnosis is beneficial due to versatility, non-invasive procedure, and rapid results generated. It also has applications in research and education of diseases.
- Purpose of the Article: AI integration into medical diagnostics can maximize the workflow efficiencies thereby improving quality. The article discusses the role of AI in clinical diagnosis, creating an awareness about AI for medical professionals.
The Role of AI in X-Ray Diagnosis
- How AI Enhances X-Ray Imaging: Embedded AI in imaging devices enhances the quality of images and streamlines workflow by offering real-time feedback. For technologists, AI can improve the X-ray workflow, particularly in image acquisition, resulting in images with greater clarity and resolution, while also reducing noise.
- AI enhances diagnostic accuracy by recognizing patterns that human eyes might miss and early detection of minute signs. On-device AI can automate repetitive imaging tasks. The AI systems can alert the technologists if there are any deviations from the protocols that can compromise the quality. AI Automation of image rotations can save time for technologists to prepare the images for the radiologist.
- Types of AI Algorithms Used in X-Ray Diagnosis
- Machine Learning: machine learning plays a pivotal role in AI-driven X-ray diagnosis. Machine learning algorithms are based on predefined engineering features based on expert knowledge. This can quantify radiographic features like, 3d shape of tumors or in histogram. Only the suitable features are selected, and the data are incorporated into the statistical machine learning models for identification of disease biomarkers.
- Deep Learning: deep learning algorithms learn directly through the data sets making them superior. They have superior problem-solving capabilities and do not require predefined features. Example of a deep learning algorithm in medical imaging is Convolutional neural network (CNN). CNN has several layers, including hidden layers that perform pooling operations. These are connected to layers that provide reasoning and ultimately make diagnostic predictions through an output layer. Image input to the series of layers provides a high-quality end point with learning of imaging features. CNN uses labeled data for supervised learning, while autoencoders use unlabeled data because they require unsupervised learning.
Accuracy of AI in X-Ray Diagnosis
- Comparison with Traditional Methods: Radiologists rely on their expertise for traditional x-ray diagnosis, but they can make errors. In contrast, AI, especially machine learning and deep learning models, analyzes x-ray images with precision by recognizing patterns in vast datasets that humans might miss.
- Statistical Data on AI Accuracy: Researchers tested an AI model with 207 image challenge questions from the New England Journal of Medicine to assess the diagnostic abilities of healthcare professionals. The AI model provided answers with rationale, relevant medical knowledge, and description for each image. The AI model’s performance was evaluated by 9 physicians, and they found that the AI model and physicians selected the correct diagnosis. However, the ability of AI was inconsistent with diagnosis.
- Real-World Applications: Clinical AI applications are possible especially in case of oncology diagnosis. The radiology-based AI application in thoracic imaging, abdominal and pelvic imaging, colonoscopy, mammography, and brain imaging. AI has diverse applications in healthcare, such as automating radiation therapy, dose optimization, and monitoring treatment success in radiation oncology. In dermatology, AI uses deep learning to diagnose skin cancers by identifying subtle features invisible to the naked eye. In pathology, it quantifies biopsy sample images for cancer detection. Furthermore, AI aids in DNA and RNA sequencing by extracting features to detect point mutations and predict the impact on DNA and RNA binding protein sequences.
Challenges and Limitations
- Technical Challenges: The researcher from National Institute of Health (NIH) reported that AI answered all the medical quizzes for testing physicians’ ability to diagnose a disease. But the AI failed to provide a correct explanation on how it led to the correct answer.
NLM acting director, Stephen Sherry, Ph. D. stated that AI is not advanced enough to replace human intervention that is evident from the study. However, AI has the potential to assist physicians in diagnosing the disease and start the treatment faster. Another challenge is training the AI models with Data. Biases in training data can lead to diagnostic inaccuracies among different demographic groups that can lead to poor health outcomes. Incorporation of AI into the clinical workflow can be challenging. Healthcare providers need to adapt to these technologies which may require changes in their practice. AI incorporation requires computational resources and infrastructure which may not be available in all healthcare settings.
- Ethical and Legal Considerations: Use of AI with handling of patient data leads to concerns with consent and data privacy. There is a potential for misuse of sensitive information. Legal frameworks are required to address such issues.
Future Prospects
- Advancements in AI Technology: AI technology has rapidly advanced and entered the medical imaging space. Scientists are developing virtual contrast agents that can highlight hidden features in scans without using traditional contrast dyes. This can reduce risk of associated allergic reactions or side effects. AI are adept at early detection of diseases that are crucial in conditions like cancer. One of the examples is Mira, an AI breast cancer risk assessment tool that can accurately predict cancer risk and help doctors plan treatment. AI can analyze vast amounts of data to provide personalized treatment. AI wearable can be used to provide continuous health monitoring that provides real-time data which helps in early detection of diseases.
- Predictions for the Future of AI in Radiology: The integration of AI into radiological devices represents the future of medical diagnostics. Built-in AI systems in these machines enable real-time data collection under human supervision, ensuring accurate and timely results for standard patient treatment.
Conclusion
- AI in radiology will primarily focus on improving workflow by detecting disease markers early to provide timely treatment. AI is set to transform healthcare diagnostics, making it more efficient, accurate, and personalized. However, it is necessary to address the associated limitations and challenges to utilize it with full potential.
References
- Artificial intelligence in radiology – PMC (nih.gov)
- AI-based radiodiagnosis using chest X-rays: A review – PubMed (nih.gov)
- AI in X-ray: Increasing quality assurance and workflow efficiencies | GE HealthCare (United States)
- NIH findings shed light on risks and benefits of integrating AI into medical decision-making | National Institutes of Health (NIH)
- Artificial intelligence in health care: benefits and challenges of machine learning technologies for medical diagnostics : report to congressional requesters – Digital Collections – National Library of Medicine (nih.gov)
- AI’s keen diagnostic eye (nature.com)
Written By: Ayoob Mansoor, PharmD, RPh